eigenvect: Eigenvector Centrality Calculation
In lwa19/centrality: Compute network centrality without generating a graph object
Description
Usage
Arguments
Value
Examples
Description
eigenvect calculates the Eigenvector Centrality of the Graph;
a node with high eigenvector centrality is more closely
connected to other central nodes
Usage
1
eigenvect(A.mat, weight = F)
Arguments
A.mat
An n x n adjacency matrix.
weight
A boolean indicating if edges are regarded as weighted;
default is FALSE
Value
Returns a length-n vector c.eig.std of
standardized eigenvector centrality scores
Examples
1
2
3
4
5
A.mat.unw = sim_adjacency(10) # edges unweighted
eigenvect(A.mat.unw)
A.mat.w = sim_adjacency(10, weight = c(1, 10))
eigenvect(A.mat.w, weight = TRUE)
lwa19/centrality documentation built on Dec. 21, 2021, 12:45 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Description Usage Arguments Value Examples
Description
eigenvect calculates the Eigenvector Centrality of the Graph;
a node with high eigenvector centrality is more closely
connected to other central nodes
Usage
1 | eigenvect(A.mat, weight = F)
|
Arguments
A.mat |
An n x n adjacency matrix. |
weight |
A boolean indicating if edges are regarded as weighted; default is FALSE |
Value
Returns a length-n vector c.eig.std of
standardized eigenvector centrality scores
Examples
1 2 3 4 5 | A.mat.unw = sim_adjacency(10) # edges unweighted
eigenvect(A.mat.unw)
A.mat.w = sim_adjacency(10, weight = c(1, 10))
eigenvect(A.mat.w, weight = TRUE)
|
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.